




"""simple docstring"""







import argparse
import os

import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints

from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder


a  =				'base_with_context'





def 				lowercase       (snake_case__ :			str   ,							snake_case__ :			List[str]			)  ->	Dict:


				'''simple docstring'''






				lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""]			)			)
				lowerCAmelCase						=					nn.Parameter(
				    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]			)   ,							requires_grad=_A			)
				for lyr_num, lyr in enumerate(model.encoders			):
								lowerCAmelCase						=					weights[f'''layers_{lyr_num}''']
								lowerCAmelCase						=					nn.Parameter(
								    torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]			)			)

								lowerCAmelCase						=					ly_weight['attention']
								lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T			)			)
								lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T			)			)
								lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T			)			)
								lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T			)			)

								lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]			)			)

								lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T			)			)
								lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T			)			)
								lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T			)			)

				lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]			)			)
				return model





def 				lowercase       (snake_case__ :			int   ,							snake_case__ :			Optional[Any]			)  ->	List[str]:


				'''simple docstring'''






				lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T			)			)

				lowerCAmelCase						=					nn.Parameter(
				    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]			)   ,							requires_grad=_A			)

				for lyr_num, lyr in enumerate(model.encoders			):
								lowerCAmelCase						=					weights[f'''layers_{lyr_num}''']
								lowerCAmelCase						=					ly_weight['attention']

								lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T			)			)
								lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T			)			)
								lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T			)			)
								lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T			)			)
								lowerCAmelCase						=					nn.Parameter(
								    torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]			)			)

								lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T			)			)
								lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T			)			)
								lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T			)			)
								lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]			)			)

				lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]			)			)

				return model





def 				lowercase       (snake_case__ :			List[Any]   ,							snake_case__ :			Dict			)  ->	Dict:


				'''simple docstring'''






				lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T			)			)
				lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T			)			)

				lowerCAmelCase						=					nn.Parameter(
				    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]			)   ,							requires_grad=_A			)

				lowerCAmelCase						=					nn.Parameter(
				    torch.FloatTensor(weights["""continuous_inputs_projection"""]["""kernel"""].T			)			)

				for lyr_num, lyr in enumerate(model.decoders			):
								lowerCAmelCase						=					weights[f'''layers_{lyr_num}''']
								lowerCAmelCase						=					nn.Parameter(
								    torch.FloatTensor(ly_weight["""pre_self_attention_layer_norm"""]["""scale"""]			)			)

								lowerCAmelCase						=					nn.Parameter(
								    torch.FloatTensor(ly_weight["""FiLMLayer_0"""]["""DenseGeneral_0"""]["""kernel"""].T			)			)

								lowerCAmelCase						=					ly_weight['self_attention']
								lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T			)			)
								lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T			)			)
								lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T			)			)
								lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T			)			)

								lowerCAmelCase						=					ly_weight['MultiHeadDotProductAttention_0']
								lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T			)			)
								lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T			)			)
								lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T			)			)
								lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T			)			)
								lowerCAmelCase						=					nn.Parameter(
								    torch.FloatTensor(ly_weight["""pre_cross_attention_layer_norm"""]["""scale"""]			)			)

								lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]			)			)
								lowerCAmelCase						=					nn.Parameter(
								    torch.FloatTensor(ly_weight["""FiLMLayer_1"""]["""DenseGeneral_0"""]["""kernel"""].T			)			)
								lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T			)			)
								lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T			)			)
								lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T			)			)

				lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(weights["""decoder_norm"""]["""scale"""]			)			)

				lowerCAmelCase						=					nn.Parameter(torch.FloatTensor(weights["""spec_out_dense"""]["""kernel"""].T			)			)

				return model





def 				lowercase       (snake_case__ :			Optional[Any]			)  ->	List[Any]:


				'''simple docstring'''






				lowerCAmelCase						=					checkpoints.load_tax_checkpoint(args.checkpoint_path			)
				lowerCAmelCase						=					jnp.tree_util.tree_map(onp.array   ,							_A			)

				lowerCAmelCase						=					[
				    'from __gin__ import dynamic_registration',
				    'from music_spectrogram_diffusion.models.diffusion import diffusion_utils',
				    'diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0',
				    'diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()',
				]

				lowerCAmelCase						=					os.path.join(args.checkpoint_path   ,							""".."""   ,							"""config.gin"""			)
				lowerCAmelCase						=					inference.parse_training_gin_file(_A   ,							_A			)
				lowerCAmelCase						=					inference.InferenceModel(args.checkpoint_path   ,							_A			)

				lowerCAmelCase						=					DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""   ,							variance_type="""fixed_large"""			)

				lowerCAmelCase						=					SpectrogramNotesEncoder(
				    max_length=synth_model.sequence_length["""inputs"""]   ,							vocab_size=synth_model.model.module.config.vocab_size   ,							d_model=synth_model.model.module.config.emb_dim   ,							dropout_rate=synth_model.model.module.config.dropout_rate   ,							num_layers=synth_model.model.module.config.num_encoder_layers   ,							num_heads=synth_model.model.module.config.num_heads   ,							d_kv=synth_model.model.module.config.head_dim   ,							d_ff=synth_model.model.module.config.mlp_dim   ,							feed_forward_proj="""gated-gelu"""   ,							)

				lowerCAmelCase						=					SpectrogramContEncoder(
				    input_dims=synth_model.audio_codec.n_dims   ,							targets_context_length=synth_model.sequence_length["""targets_context"""]   ,							d_model=synth_model.model.module.config.emb_dim   ,							dropout_rate=synth_model.model.module.config.dropout_rate   ,							num_layers=synth_model.model.module.config.num_encoder_layers   ,							num_heads=synth_model.model.module.config.num_heads   ,							d_kv=synth_model.model.module.config.head_dim   ,							d_ff=synth_model.model.module.config.mlp_dim   ,							feed_forward_proj="""gated-gelu"""   ,							)

				lowerCAmelCase						=					TaFilmDecoder(
				    input_dims=synth_model.audio_codec.n_dims   ,							targets_length=synth_model.sequence_length["""targets_context"""]   ,							max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time   ,							d_model=synth_model.model.module.config.emb_dim   ,							num_layers=synth_model.model.module.config.num_decoder_layers   ,							num_heads=synth_model.model.module.config.num_heads   ,							d_kv=synth_model.model.module.config.head_dim   ,							d_ff=synth_model.model.module.config.mlp_dim   ,							dropout_rate=synth_model.model.module.config.dropout_rate   ,							)

				lowerCAmelCase						=					load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""]   ,							_A			)
				lowerCAmelCase						=					load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""]   ,							_A			)
				lowerCAmelCase						=					load_decoder(ta_checkpoint["""target"""]["""decoder"""]   ,							_A			)

				lowerCAmelCase						=					OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder"""			)

				lowerCAmelCase						=					SpectrogramDiffusionPipeline(
				    notes_encoder=_A   ,							continuous_encoder=_A   ,							decoder=_A   ,							scheduler=_A   ,							melgan=_A   ,							)
				if args.save:
								pipe.save_pretrained(args.output_path			)


if __name__ == "__main__":
		a  =				argparse.ArgumentParser()

		parser.add_argument('--output_path', default=None, type=str, required=True, help='Path to the converted model.')
		parser.add_argument(
		    '--save', default=True, type=bool, required=False, help='Whether to save the converted model or not.'
		)
		parser.add_argument(
		    '--checkpoint_path',
		    default=f"""{MODEL}/checkpoint_500000""",
		    type=str,
		    required=False,
		    help='Path to the original jax model checkpoint.',
		)
		a  =				parser.parse_args()

		main(args)










'''simple docstring'''






import os

try:
     from .build_directory_md import good_file_paths
except ImportError:
     from build_directory_md import good_file_paths  # type: ignore

lowerCAmelCase:   Optional[int]					=		list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

lowerCAmelCase:   Optional[Any]					=		[file for file in filepaths if file != file.lower()]
if upper_files:
     print(F"{len(upper_files)} files contain uppercase characters:")
     print('\n'.join(upper_files) + '\n')

lowerCAmelCase:   int					=		[file for file in filepaths if ' ' in file]
if space_files:
     print(F"{len(space_files)} files contain space characters:")
     print('\n'.join(space_files) + '\n')

lowerCAmelCase:   Union[str, Any]					=		[file for file in filepaths if '-' in file]
if hyphen_files:
     print(F"{len(hyphen_files)} files contain hyphen characters:")
     print('\n'.join(hyphen_files) + '\n')

lowerCAmelCase:   Union[str, Any]					=		[file for file in filepaths if os.sep not in file]
if nodir_files:
     print(F"{len(nodir_files)} files are not in a directory:")
     print('\n'.join(nodir_files) + '\n')

lowerCAmelCase:   int					=		len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
     import sys

     sys.exit(bad_files)